An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection

Development of a mechanism for reconstruction of terahertz single-frequency images of biological samples

Algorithmic mechanisms are used to improve terahertz (THz) image quality, which is critical to a biological sample analysis. A complete mechanism for the super-resolution reconstruction and evaluation of THz biological sample images was constructed in this study. With eucalyptus leaves as an example, the THz spectral region screening technique was adopted to select the characteristic frequencies for imaging, and the THz single-frequency images were reconstructed with the single-image super-resolution image reconstruction technique. The THz super-resolution reconstructed images without ideal reference were evaluated after the introduction of three no-reference image evaluation criteria considering the diversity and complexity of organisms. The results show that the THz image reconstruction mechanism proposed in this study led to an increase in resolution and a decrease in noise. At the same time, the imaging quality of biological samples was considerably improved, and the detailed information was enriched. These provide a reference for a THz imaging analysis of leaves and other biological samples.

Developmental neurotoxicity of silver nanoparticles: the current state of knowledge and future directions

The increasing production and use of silver nanoparticles (AgNPs) as an antimicrobial agent in an array of medical and commercial products, including those designed for infants and children, poses a substantial risk of exposure during the developmental period. This review summarizes current knowledge on developmental neurotoxicity of AgNPs in both pre- and post-natal stages with a focus on the biological specificity of immature organisms that predisposes them to neurotoxic insults as well as the molecular mechanisms underlying AgNP-induced neurotoxicity. The current review revealed that AgNPs increase the permeability of the blood-brain barrier (BBB) and selectively damage neurons in the brain of immature rats exposed pre and postnatally. Among the AgNP-induced molecular mechanisms underlying toxic insult is cellular stress, which can consequently lead to cell death. Glutamatergic neurons and NMDAR-mediated neurotransmission also appear to be a target for AgNPs during the postnatal period of exposure. Collected data indicate also that our current knowledge of the impact of AgNPs on the developing nervous system remains insufficient and further studies are required during different stages of development with investigation of environmentally-relevant doses of exposure.

Toxicological assessment of agrochemicals on bees using machine learning tools

Machine learning (ML) is a branch of artificial intelligence (AI) that enables the analysis of complex multivariate data. ML has significant potential in risk assessments of non-target insects for modeling the multiple factors affecting insect health, including the adverse effects of agrochemicals. Here, the potential of ML for risk assessments of glyphosate (herbicide; formulation) and imidacloprid (insecticide, neonicotinoid; formulation) on the stingless bee Melipona quadrifasciata was explored. The collective behavior of forager bees was analyzed after in vitro exposure to agrochemicals. ML algorithms were applied to identify the agrochemicals that the bees have been exposed to based on multivariate behavioral features. Changes in the in situ detection of different proteins in the midgut were also studied. Imidacloprid exposure leads to the greatest changes in behavior. The ML algorithms achieved a higher accuracy (up to 91%) in identifying agrochemical contamination. The two agrochemicals altered the detection of cells positive for different proteins, which can be detrimental to midgut physiology. This study provides a holistic assessment of the sublethal effects of glyphosate and imidacloprid on a key pollinator. The procedures used here can be applied in future studies to monitor and predict multiple environmental factors affecting insect health in the field.

Foraging and Drifting Patterns of the Highly Eusocial Neotropical Stingless Bee Melipona fasciculata Assessed by Radio-Frequency Identification Tags

Bees play a key role in ecosystem services as the main pollinators of numerous flowering plants. Studying factors influencing their foraging behavior is relevant not only to understand their biology, but also how populations might respond to changes in their habitat and to the climate. Here, we used radio-frequency identification tags to monitor the foraging behavior of the neotropical stingless bee Melipona fasciculata with special interest in drifting patterns i.e., when a forager drifts into a foreign nest. In addition, we collected meteorological data to study how abiotic factors affect bees’ activity and behavior. Our results show that only 35% of bees never drifted to another hive nearby, and that factors such as temperature, humidity and solar irradiation affected the bees drifting rates and/or foraging activity. Moreover, we tested whether drifting levels would decrease after marking the nest entrances with different patterns. However, contrary to our predictions, there was an increase in the proportion of drifting, which could indicate factors other than orientation mistakes playing a role in this behavior. Overall, our results demonstrate how managed bee populations are affected by both nearby hives and climate factors, offering insights on their biology and potential commercial application as crop pollinators.

Analysis of Honeybee Drone Activity during the Mating Season in Northwestern Argentina

Males in Hymenopteran societies are understudied in many aspects and it is assumed that they only have a reproductive function. We studied the time budget of male honey bees, drones, using multiple methods. Changes in the activities of animals provide important information on biological clocks and their health. Yet, in nature, these changes are subtle and often unobservable without the development and use of modern technology. During the spring and summer mating season, drones emerge from the hive, perform orientation flights, and search for drone congregation areas for mating. This search may lead drones to return to their colony, drift to other colonies (vectoring diseases and parasites), or simply get lost to predation. In a low percentage of cases, the search is successful, and drones mate and die. Our objective was to describe the activity of Apis mellifera drones during the mating season in Northwestern Argentina using three methods: direct observation, video recording, and radio frequency identification (RFID). The use of RFID tagging allows the tracking of a bee for 24 h but does not reveal the detailed activity of drones. We quantified the average number of drones' departure and arrival flights and the time outside the hive. All three methods confirmed that drones were mostly active in the afternoon. We found no differences in results between those obtained by direct observation and by video recording. RFID technology enabled us to discover previously unknown drone behavior such as activity at dawn and during the morning. We also discovered that drones may stay inside the hive for many days, even after initiation of search flights (up to four days). Likewise, we observed drones to leave the hive for several days to return later (up to three days). The three methods were complementary and should be considered for the study of bee drone activity, which may be associated with the diverse factors influencing hive health.

Honey Bee Colony Population Daily Loss Rate Forecasting and an Early Warning Method Using Temporal Convolutional Networks

The population loss rate of a honey bee colony is a critical index to verify its health condition. Forecasting models for the population loss rate of a honey bee colony can be an essential tool in honey bee health management and pave a way to early warning methods in the understanding of potential abnormalities affecting a honey bee colony. This work presents a forecasting and early warning algorithm for the population daily loss rate of honey bee colonies and determining warning levels based on the predictions. Honey bee colony population daily loss rate data were obtained through embedded image systems to automatically monitor in real-time the in-and-out activity of honey bees at hive entrances. A forecasting model was trained based on temporal convolutional neural networks (TCN) to predict the following day’s population loss rate. The forecasting model was optimized by conducting feature importance analysis, feature selection, and hyperparameter optimization. A warning level determination method using an isolation forest algorithm was applied to classify the population daily loss rate as normal or abnormal. The integrated algorithm was tested on two population loss rate datasets collected from multiple honey bee colonies in a honey bee farm. The test results show that the forecasting model can achieve a weighted mean average percentage error (WMAPE) of 17.1 ± 1.6%, while the warning level determination method reached 90.0 ± 8.5% accuracy. The forecasting model developed through this study can be used to facilitate efficient management of honey bee colonies and prevent colony collapse.

Project IPAD, a database to catalogue the analysis of Fukushima Daiichi accident fragmental release material

The 2011 accident at Japan’s Fukushima Daiichi Nuclear Power Plant released a considerable inventory of radioactive material into the local and global environments. While the vast majority of this contamination was in the form of gaseous and aerosol species, of which a large component was distributed out over the neighbouring Pacific Ocean (where it was subsequently deposited), a substantial portion of the radioactive release was in particulate form and was deposited across Fukushima Prefecture. To provide an underpinning understanding of the dynamics of this catastrophic accident, alongside assisting in the off-site remediation and eventual reactor decommissioning activities, the ‘International Particle Analysis Database’, or ‘IPAD’, was established to serve as an interactive repository for the continually expanding analysis dataset of the sub-mm ejecta particulate. In addition to a fully interrogatable database of analysis results for registered users (exploiting multiple search methods), the database also comprises an open-access front-end for members of the public to engage with the multi-national analysis activities by exploring a streamlined version of the data.

Measurement(s)	activity (of a radionuclide) • composition • isotopic ratio
Technology Type(s)	radioactivity measurement method • gamma-ray spectroscopy • transmission electron microscopy with EDAX • X-ray diffraction • micro-computed tomography • X-ray fluorescence microscopy • X-ray absorption spectroscopy • Raman spectroscopy • proton-induced X-ray emission spectroscopy • alpha-particle spectroscopy • beta-particle spectroscopy • secondary ion mass spectrometry • inductively coupled plasma mass spectrometry • thermal ionisation mass spectrometry • three dimensional-atom probe tomography • resonance ionisation mass spectrometry
Sample Characteristic - Environment	nuclear power plant
Sample Characteristic - Location	Fukushima Prefecture

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12821081